DANL Project

Data-Driven Mastery: Road to excellence

0.1 Introduction

About this project 👏

In this project my group and I are using a data set of motor cars to determine the fastest cars, then we will decipher some parameters by what makes them the best.

1 Data

data("mtcars")

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

1.1 Summary Statistics

rmarkdown::paged_table(mtcars)

1.1.1 Summary Statistics

skim(mtcars) %>% 
  select(-n_missing)

Data summary

Name	mtcars
Number of rows	32
Number of columns	11
_______________________
Column type frequency:
numeric	11
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

2 Visualization #1

car_models <- rownames(mtcars)

ggplot(mtcars, aes(x = reorder(car_models, -qsec), y = qsec)) +
  geom_point(color = "blue") +
  labs(title = "1/4 Mile Times by Car Name",
       x = "Car Name",
       y = "1/4 Mile Time") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

car_models <- rownames(mtcars)
mtcars$cyl <- factor(mtcars$cyl)

2.1 Explanation

This graph gives a visual representation of the quarter mile times of various motor cars in descending order.

3 Visualization #2

ggplot(mtcars, aes(x = factor(cyl), y = qsec, fill = factor(cyl))) +
  geom_boxplot(alpha = 0.7) +
  geom_point(position = position_jitter(width = 0.3), color = "black", size = 2) +
  labs(title = "1/4 Mile Time by Number of Cylinders",
       x = "Number of Cylinders",
       y = "1/4 Mile Time",
       fill = "Number of Cylinders") +
  scale_fill_discrete(name = "Number of Cylinders") +
  theme_minimal()

3.1 Explanation

This graph allows you to visually see the difference in quarter mile time and how it varies by the number of cylinders in the vehicle. As you can see the vehicles with fewer cylinders can generally be seen with a slower quarter mile time.

4 Visualization #3

ggplot(mtcars, aes(x = wt, y = qsec, color = cyl)) +
  geom_point() +
  labs(title = "Scatter Plot of Quarter Mile Time vs. Weight by Number of Cylinders", 
       x = "Weight (1000 lbs)",
       y = "Quarter Mile Time") +
  scale_color_discrete(name = "Number of Cylinders")

4.1 Explanation

This visualization is similar to the last one except that this graph also shows the weight distribution among the vehicles, generally we can see that cars with more cylinders will be heavier than their counterparts.

5 Visualization #4

ggplot(mtcars, aes(x = hp, y = qsec, color = factor(vs))) +
  geom_point(size = 3) +
  labs(title = "Quarter Mile Time by Horsepower and Engine Type",
       x = "Horsepower",
       y = "Quarter Mile Time") +
  scale_color_discrete(name = "Engine Type", labels = c("V-shaped", "Straight")) +
  theme_minimal()

5.1 Explanation

Here we can decipher the quarter mile time in terms of the vehicles horse power and engine type, as we can see, as horse power grows, the quarter mile time goes down. At the same time most vehicles contain a V-Shaped engine that how the faster quarter mile times.

6 Visualization #5

library(dplyr)
library(ggplot2)

top_5_fastest <- mtcars %>%
  arrange(qsec) %>%
  head(5)

print(top_5_fastest[, c("mpg", "cyl", "hp", "wt", "qsec")])

                mpg cyl  hp   wt  qsec
Ford Pantera L 15.8   8 264 3.17 14.50
Maserati Bora  15.0   8 335 3.57 14.60
Camaro Z28     13.3   8 245 3.84 15.41
Ferrari Dino   19.7   6 175 2.77 15.50
Duster 360     14.3   8 245 3.57 15.84

top_5_fastest$car_names <- rownames(top_5_fastest)

variables_to_plot <- c("cyl", "hp", "wt")
plot_list <- lapply(variables_to_plot, function(var) {
  ggplot(top_5_fastest, aes(x = reorder(car_names, qsec), y = get(var), fill = factor(cyl))) +
    geom_bar(stat = "identity", position = "dodge") +
    labs(title = paste("Top 5 Fastest Cars -", var),
         x = "Car",
         y = var,
         fill = "Cylinders") +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
})

library(patchwork)
plot_list[[1]] + plot_list[[2]] + plot_list[[3]]

6.1 Explanation

Finally, this visualization shows how 3 variables (weight, cylinders, and horsepower) differ in each fastest car (from left to right).